Keywords:
- https://publications.hevs.ch/index.php/keywords/single/197
- 3D imaging
- 3D information retrieval
- 3D texture
- 82Rb PET/CT
- AI
- ARC
- artificial intelligence
- Atlas
- Automatic segmentation
- Benchmarking
- big data
- Biological tissue
- Brain
- CAD
- case-based retrieval
- Challenge
- Classification
- clinical data
- clinical data analysis
- clinical workflows
- Computer Vision and Pattern Recognition (cs.CV)
- computer-aided diagnosis
- computerised tomography
- computing infrastructures
- Content-based image retrieval
- conversation analysis
- Cortical lesions
- Cox Model
- cs.CV
- data mining
- Deep convolutional neural network
- Deep Learning
- Desktop Grid
- detection
- Discrete wavelet transform
- eHealth
- Epilepsy
- ethnomethodology
- evaluation
- exoticism
- feature extraction
- FOS: Computer and information sciences
- fracture retrieval
- Grid
- Hadoop
- head and neck cancer
- Healthcare
- HealthGrid
- High-resolution lung CT
- Hospital
- Human-Centered Computing
- image acquisition
- image analysis
- image classif
- image classification
- Image databases
- image processing
- image retrieval
- image storage
- ImageCLEF
- information fusion
- information retrieval
- information retrieval evaluation
- information retrieval literature
- Information Systems
- Infrastructures for computation
- interstitial lung diseases
- Left Ventricle Segmentation
- Lesion detection
- Lesion segmentation
- lung
- Lung image
- Lung image analysis
- Lung image retrieval
- lung segmentation
- lung tissue classification
- machine learning
- Machine Learning (cs.LG)
- Magnetic resonance imaging
- MapReduce
- medical image analysis
- Medical image analysis and retrieval
- medical image processing
- Medical image retrieval
- medical imaging
- Medical informatics
- Medical information retrieval
- mentalism
- mobile devices
- mobile information retrieval
- MRI
- multi-atlas based segmentation
- multidimensional image data analysis
- multimedia library
- Multimodal information retrieval and information fusion
- Multiple sclerosis
- Myocardial Blood Flow
- nosocomial infection
- oncology
- organ segmentation
- Oropharynx
- radiomics
- retrieval
- Riesz
- Riesz transform
- Rubidium PET
- scalability
- Security
- segmentation
- signal processing
- social interaction
- support vector machines
- Systematic Review
- Taverna
- technologism
- test collection
- test collection creation including signals and images
- texture analysis
- texture classification
- Transmural Perfusion
- Trustworthy AI
- user interface
- user interfaces
- User testing and task analysis
- virtualization
- visceral-project
- visual feature extraction
- visual inforamtion retrieval
- visual information retrieval
- wavelet
- wavelets
- Yearbook
Publications of Adrien Depeursinge sorted by title
F
, , and , Fusing Learned Representations from Riesz and Deep CNNs for Lung Tissue Classification (2019), in: Medical Image Analysis, 56(172-183)
|
[URL] |
| , , , , , and , Fusing visual and clinical information for lung tissue classification in high-resolution computed tomography (2010), in: Artificial Intelligence in Medicine, 50:1(13--21) |
[DOI] [URL] |
| and , Fusion Techniques for Combining Textual and Visual Information Retrieval, in: ImageCLEF, pages 95-114, Springer Berlin Heidelberg, 2010 |
[DOI] |
G
| , , , , and , GPU-Accelerated Texture Analysis Using Steerable Riesz Wavelets, in: 24th IEEE Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, Heraklion Crete, Greece, pages 431--434, 2016 |
|
| , , , and , Grid Computing Inside Hospitals using Virtualization Technology: A secure solution for heavy computing tasks, in: HUG Research Day 2009, 2009 |
|
| , , , and , Grid Computing Inside Hospitals using Virtualization Technology: A secure solution for heavy computing tasks, in: Annual Conference for Swiss Society of Medical Informatics, Bern, Switzerland, 2009 |
|
| , , , and , Guide for radiologists and nuclear medicine physicians for a standardized radiomics analysis, in: Swiss Congress of Radiology (SCR) 2023, 2023 |
|
H
| , , , , , , , , , , , , , , , , , , , , , , , , , , , , and , HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT, 2026 |
[URL] |
| Head and Neck Tumor Segmentation, Springer International Publishing, 2021 |
[URL] |
| Head and Neck Tumor Segmentation and Outcome Prediction, Springer International Publishing, 2022 |
[DOI] [URL] |
| , , and , Head and Neck Tumor Segmentation and Outcome Prediction, Springer International Publishing, 2023 |
[DOI] [URL] |
| , , , , , , and , HEad and neCK TumOR segmentation and outcome prediction using AI: lessons from three consecutive years of the HECKTOR challenge, in: European Head and Neck Society (EHNS) on Artificial Intelligence (AI) in Head & Neck Oncology, Lausanne and virtual, 2023 |
|
| , , , , , , , and , HEad and neCK TumOR segmentation and outcome prediction: The HECKTOR challenge, in: European Society of Radiology, 2022 |
|
, , , , , , , , , , , , , , , , , , , , , , , , , and , Head and Neck Tumor Segmentation in PET/CT: The HECKTOR Challenge (2022), in: Medical Image Analysis, 77(102336)
|
[URL] |
| , , and , Hierarchic Anatomical Structure Segmentation Guided by Spatial Correlations (AnatSeg-Gspac): VISCERAL Anatomy3, in: Proceedings of the VISCERAL Anatomy Grand Challenge at the 2015 IEEE ISBI, pages 22-26, CEUR-WS, 2015 |
[URL] |
, and , Hierarchical classification using a frequency-based weighting and simple visual features (2008), in: Pattern Recognition Letters, 29:15(2011-2017)
|
|
| , , , , , , , , , , and , Holistic Classification of CT Attenuation Patterns for Interstitial Lung Diseases via Deep Convolutional Neural Networks, in: 1st Workshop on Deep Learning in Medical Image Analysis, Münich, Germany, pages 41-48, 2015 |
|
| , , , , , , , and , Holographic visualisation and interaction of fused CT, PET and MRI volumetric medical imaging data using dedicated remote GPGPU ray casting, in: Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation, pages 102-110, Springer International Publishing, 2018 |
|
| , , , , and , How clinical information systems can support life science research (2008), in: Swiss Medical Informatics, 64(21-24) |
|
, , , , , , and , How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review (2023), in: NeuroImage: Clinical, 39(103491)
|
[DOI] [URL] |
| , , , , , , and , How to find the best radiomics features for prediction of overall survival in SBRT for hepatocellular carcinoma?, in: European SocieTy for Radiotherapy & Oncology, 2019 |
|
I
| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and , Identification of paramagnetic rim lesions using conventional MRI - a deep learning approach, in: 39th Congress Of The European Committee For Treatment And Research In Multiple Sclerosis (ECTRIMS), 2023 |
|
| , , , , and , Image-based diagnostic aid for interstitial lung disease with secondary data integration, in: SPIE Medical Imaging, 2007 |
|
| , , , , , , and , Impact of a Gaussian filter applied to post-reconstruction PET images on radiomic features to predict complete pathological response in breast cancer (2020), in: Journal of Nuclear Medicine, 61:supplement 1(606--606) |
[URL] |
| , , , , , , and , Impact of a Gaussian filter applied to post-reconstruction PET on radiomic features in assessing tumor heterogeneity in breast cancer. (2020), in: Journal of Nuclear Medicine, 61:supplement 1(612--612) |
[URL] |
| , , , , , , , , , , , and , Impact of CT dose on AI performance: A comparison of radiomics, deep, and foundation models in a multi-centric anthropomorphic phantom study (2026), in: Medical Physics, 53:3(e70374) |
|
| , , , , , , , , , , , and , Impact of deep learning segmentation methods on the robustness of MR glioblastoma radiomics, in: 2022 Annual Meeting of the European Society of Radiation Oncology (ESTRO), 2022 |
|
| , , , , , , , and , Influence of CT Scanners on Radiomics Features in Abdominal CT: A Multicenter Phantom Study, in: European Congress of Radiology, 2024 |
|
| , and , Information Fusion for Combining Visual and Textual Image Retrieval, in: 20th IEEE International Conference on Pattern Recognition (ICPR), Istanbul, Turkey, pages 1590--1593, 2010 |
|
| , , , , , , , and , Instance-level explanations in multiple sclerosis lesion segmentation: a novel localized saliency map, in: ISMRM 2024, 2024 |
[URL] |
| , , , , , , , and , Instance-level quantitative saliency in multiple sclerosis lesion segmentation (2026), in: Nature Scientific Reports, 16(6917) |
[DOI] [URL] |
| , , , , , , , and , Integrating MRI and PET/CT Radiomics for Enhanced Survival Prediction in Esophageal Cancer, in: European Congress of Radiology, 2025 |
|
| , , , and , Integrating radiomics into holomics for personalised oncology: from algorithms to bedside (2020), in: European Radiology Experimental, 4(11) |
|
| , , , , , , , , , and , Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis, 2024 |
|
| , , , and , Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging, in: Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2020, 2020 |
|
| , and , Interpretable Regime Trajectories via Generative Graph State-Space Models, in: New Perspectives in Graph Machine Learning NPGML at NeurIPS 2025, 2025 |
[URL] |
| , , , , and , Is tumor heterogeneity quantified by 3D texture analysis of MRI able to predict non-response to NAC in breast cancer?, in: European Society for Magnetic Resonance in Medicine and Biology, 2016 |
|
K
| , , , , and , KnowARC: Enabling Grid Networks for the Biomedical Research Community, in: Healthgrid 2007, pages 261-268, 2007 |
|
L
| and , La recherche d’images en plusieurs dimensions (2011), in: Market/IBCom |
|
| , and , Learning Cross-Protocol Radiomics and Deep Feature Standardization from CT Images of Texture Phantoms, in: SPIE Medical Imaging 2019, International Society for Optics and Photonics, pages 109-116, SPIE, 2019 |
|
| , , , , , , , , and , Left Ventricle Segmentation in Dynamic 82Rb PET/CT Using Deep Convolutional Neural Networks, Annual Congress of the European Association of Nuclear Medicine (EANM), 2025 |
|
| , , , , , and , LEXU: Learning from Expert Disagreement for Single-Pass Uncertainty Estimation in Medical Image Segmentation, in: International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, 2025 |
|
, , , and , Local Rotation Invariance in 3D CNNs (2020), in: Medical Image Analysis, 65(101756)
|
[DOI] [URL] |
| , , , , , , , , and , Locoregional radiogenomic models to capture gene expression heterogeneity in glioblastoma (2018), in: biorXiv |
[DOI] [URL] |
| , , , , , , , , , and , Lung lesion detectability on decimated and CNN-based denoised 18F-FDG PET/CT, in: Swiss Congress of Radiology, 2024 |
|
| , , , , , , , , , and , Lung lesion detectability on images obtained from decimated and CNN-based denoised [18F]-FDG PET/CT scan: An observer-based study for lung-cancer screening (2025), in: European Journal of Nuclear Medicine and Molecular Imaging |
[URL] |